Scaling genetic programming for data classification using mapreduce methodology, Fifth World Congress on Nature and Biologically Inspired Computing, pp.132-139, 2013. ,
Searching for exotic particles in high-energy physics with deep learning, Nature communications, vol.5, 2014. ,
Enhanced higgs boson to ? + ? -search with deep learning, Physical review letters, vol.114, issue.11, pp.111-801, 2015. ,
ECJ+HADOOP: an easy way to deploy massive runs of evolutionary algorithms, Applications of Evolutionary Computation, EvoApplications 2016, vol.9598, pp.91-106, 2016. ,
Mapreduce: Simplified data processing on large clusters, 6th Symposium on Operating System Design and Implementation (OSDI 2004), pp.137-150, 2004. ,
DEAP: Evolutionary algorithms made easy, Journal of Machine Learning Research, vol.13, pp.2171-2175, 2012. ,
Scaling evolutionary programming with the use of apache spark, Computer Science (AGH), vol.17, issue.1, pp.69-82, 2016. ,
Dynamic training subset selection for supervised learning in genetic programming. In: Parallel Problem Solving from Nature -PPSN III, Lecture Notes in Computer Science, vol.866, pp.312-321, 1994. ,
An approach to reduce the cost of evaluation in evolutionary learning, Computational Intelligence and Bioinspired Systems, pp.804-811, 2005. ,
,
Scale genetic programming for large data sets: Case of higgs bosons classification, the 22nd International Conference, vol.126, p.2018, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-02071412
High Performance Spark, 2017. ,
Mastering Apache Spark 2.x, 2017. ,
Genetic Programming: On the Programming of Computers by Means of Natural Selection, 1992. ,
A distributed implementation using apache spark of a genetic algorithm applied to test data generation, Genetic and Evolutionary Computation Conference, pp.1857-1863, 2017. ,
Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach, Mathematical Problems in Engineering, p.11, 2015. ,
A parallel genetic algorithm based on spark for pairwise test suite generation, J. Comput. Sci. Technol, vol.31, issue.2, pp.417-427, 2016. ,
Evaluation of machine learning frameworks on bank marketing and higgs datasets, 2nd International Conference on Advances in Computing and Communication Engineering, pp.551-555, 2015. ,
Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing, Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation, pp.15-28, 2012. ,